The present invention relates generally to the field of computer based image analysis and recognition, and more particularly to robust face detection, representation, and recognition.
Face recognition is an increasingly important application of computer vision, particularly in areas such as security. However, accurate face recognition is often difficult due to the fact that a person's face can look very different depending on pose, expression, illumination, and facial accessories. Face recognition has been approached with 3D model-based techniques and feature-based methods. The essential feature of every face recognition system is the similarity measure—where faces are considered similar if they belong to the same individual. A similarity measure is a real-valued function that quantifies the similarity between two objects. Typically such measures are in some sense the inverse of distance metrics: they take on large values for similar objects and either zero or a negative value for dissimilar objects. The similarity measure can be used to verify that two face images belong to the same person, or to classify novel images by determining to which of the given faces a new example is most similar.
A face recognition system generally involves a face detection process for detecting the position and size of a face image included in an input image, a face parts detection process for detecting the positions of principal face parts from the detected face image, and a face identification process that identifies the face image (i.e., the person) by checking an image obtained by correcting the position and rotation of the face image based on the positions of the face parts against a registered image. Face detection is concerned with the problem of locating regions within a digital image or video sequence, which have a high probability of representing a human face. Face detection includes a process of determining whether a human face is present in an input image, and may include determining a position and/or other features, properties, parameters, or values of parameters of the face within the input image.
Face recognition technology has achieved tremendous advancements in the last decade. However, many current automated tools perform best on well-posed, frontal facial photos taken for identification purposes. These tools may not be able to handle the sheer volume of possibly relevant videos and photographs captured in unconstrained environments. In such environments, factors like pose, illumination, partial occlusion, and varying facial expressions present a difficult challenge, even for state of the art face recognition systems.